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Machine Learning with Python

Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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Machine Learning with PythonСітка:Machine Learning with PythonІндія5 049Освіта2 404

📈 Аналітичний огляд Telegram-каналу Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 67 812 підписників, посідаючи 2 404 місце в категорії Освіта та 5 049 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 67 812 підписників.

За останніми даними від 05 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 77, а за останні 24 години на 9, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 2.60%. Протягом перших 24 годин після публікації контент зазвичай збирає 2.50% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 1 767 переглядів. Протягом першої доби публікація в середньому набирає 1 695 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 6.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як insidead, learning, degree, evaluation, algorithm.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Завдяки високій частоті оновлень (останні дані отримано 07 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.

67 812
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+587 днів
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Machine Learning with Python
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Repost from Learn Python Coding
Checking the reliability of a password with Python! Sometimes you need to quickly check how secure a password is. Let's look at a simple example using regular expressions - a good opportunity to practice with re and conditional logic. Import the module: 
import re
Create a password check function: 
def check_password_strength(password):
    length = len(password) >= 8
    upper = re.search(r"[A-Z]", password)
    lower = re.search(r"[a-z]", password)
    digit = re.search(r"\d", password)
    special = re.search(r"[@$!%*?&]", password)

    if all([length, upper, lower, digit, special]):
        return "✅ Reliable password"
    else:
        return "⚠️ Weak password"
Check a few examples: 
print(check_password_strength("Qwerty123"))
print(check_password_strength("Qw!8zYt@1"))
Output example: 
⚠️ Weak password  
✅ Reliable password
🔥 Example of how to check a string for compliance with several conditions using code - and practice with regular expressions. 🚪 @DataScience4

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Repost from Github Top Repositories
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Repost from Github Top Repositories
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Repost from Python Courses & Resources
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Repost from Learn Python Coding
A cheat sheet about functions and techniques in Python: shows useful built-in functions, working with iterators, strings, and
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Repost from Learn Python Coding
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𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 🔥👇⁣⁣ ⁣⁣ ✅ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘸𝘢𝘴 𝘪𝘵 𝘪𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦𝘥?⁣⁣ 𝘐𝘵 𝘴𝘰𝘭𝘷𝘦𝘥 𝘵𝘩𝘦 𝘭𝘪𝘮𝘪𝘵𝘢𝘵𝘪𝘰𝘯𝘴 𝘰𝘧 𝘙𝘕𝘕𝘴 & 𝘓𝘚𝘛𝘔𝘴 𝘣𝘺 𝘶𝘴𝘪𝘯𝘨 𝘴𝘦𝘭𝘧-𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯, 𝘦𝘯𝘢𝘣𝘭𝘪𝘯𝘨 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 𝘢𝘯𝘥 𝘤𝘢𝘱𝘵𝘶𝘳𝘪𝘯𝘨 𝘭𝘰𝘯𝘨-𝘳𝘢𝘯𝘨𝘦 𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘤𝘪𝘦𝘴 𝘭𝘪𝘬𝘦 𝘯𝘦𝘷𝘦𝘳 𝘣𝘦𝘧𝘰𝘳𝘦!⁣⁣ ⁣⁣ ✅ 𝘚𝘦𝘭𝘧-𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 – 𝘛𝘩𝘦 𝘮𝘢𝘨𝘪𝘤 𝘣𝘦𝘩𝘪𝘯𝘥 𝘪𝘵⁣⁣ 𝘌𝘷𝘦𝘳𝘺 𝘸𝘰𝘳𝘥 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘴 𝘪𝘵𝘴 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘪𝘯 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯 𝘵𝘰 𝘰𝘵𝘩𝘦𝘳𝘴—𝘮𝘢𝘬𝘪𝘯𝘨 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴 𝘴𝘮𝘢𝘳𝘵𝘦𝘳 𝘢𝘯𝘥 𝘮𝘰𝘥𝘦𝘭𝘴 𝘮𝘰𝘳𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵-𝘢𝘸𝘢𝘳𝘦.⁣⁣ ⁣⁣ ✅ 𝘔𝘶𝘭𝘵𝘪-𝘏𝘦𝘢𝘥 𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 – 𝘚𝘦𝘦𝘪𝘯𝘨 𝘧𝘳𝘰𝘮 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘢𝘯𝘨𝘭𝘦𝘴⁣⁣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘩𝘦𝘢𝘥𝘴 𝘧𝘰𝘤𝘶𝘴 𝘰𝘯 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢. 𝘐𝘵’𝘴 𝘭𝘪𝘬𝘦 𝘩𝘢𝘷𝘪𝘯𝘨 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘦𝘹𝘱𝘦𝘳𝘵𝘴 𝘢𝘯𝘢𝘭𝘺𝘻𝘦 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯!⁣⁣ ⁣⁣ ✅ 𝘗𝘰𝘴𝘪𝘵𝘪𝘰𝘯𝘢𝘭 𝘌𝘯𝘤𝘰𝘥𝘪𝘯𝘨 – 𝘛𝘦𝘢𝘤𝘩𝘪𝘯𝘨 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘰𝘳𝘥𝘦𝘳 𝘮𝘢𝘵𝘵𝘦𝘳𝘴⁣⁣ 𝘚𝘪𝘯𝘤𝘦 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳𝘴 𝘥𝘰𝘯’𝘵 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘥𝘢𝘵𝘢 𝘴𝘦𝘲𝘶𝘦𝘯𝘵𝘪𝘢𝘭𝘭𝘺, 𝘵𝘩𝘪𝘴 𝘵𝘳𝘪𝘤𝘬 𝘦𝘯𝘴𝘶𝘳𝘦𝘴 𝘵𝘩𝘦𝘺 “𝘬𝘯𝘰𝘸” 𝘵𝘩𝘦 𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯 𝘰𝘧 𝘦𝘢𝘤𝘩 𝘵𝘰𝘬𝘦𝘯.⁣⁣ ⁣⁣ ✅ 𝘓𝘢𝘺𝘦𝘳 𝘕𝘰𝘳𝘮𝘢𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 – 𝘚𝘵𝘢𝘣𝘪𝘭𝘪𝘻𝘪𝘯𝘨 𝘵𝘩𝘦 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘱𝘳𝘰𝘤𝘦𝘴𝘴⁣⁣ 𝘐𝘵 𝘴𝘱𝘦𝘦𝘥𝘴 𝘶𝘱 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘢𝘯𝘥 𝘢𝘷𝘰𝘪𝘥𝘴 𝘷𝘢𝘯𝘪𝘴𝘩𝘪𝘯𝘨 𝘨𝘳𝘢𝘥𝘪𝘦𝘯𝘵𝘴, 𝘭𝘦𝘵𝘵𝘪𝘯𝘨 𝘮𝘰𝘥𝘦𝘭𝘴 𝘨𝘰 𝘥𝘦𝘦𝘱𝘦𝘳 𝘢𝘯𝘥 𝘭𝘦𝘢𝘳𝘯 𝘣𝘦𝘵𝘵𝘦𝘳.⁣⁣